R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(5
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+ ,9)
+ ,dim=c(5
+ ,156)
+ ,dimnames=list(c('handgebruik'
+ ,'ontmoeting'
+ ,'extravert'
+ ,'blozen'
+ ,'populariteit')
+ ,1:156))
> y <- array(NA,dim=c(5,156),dimnames=list(c('handgebruik','ontmoeting','extravert','blozen','populariteit'),1:156))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '5'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
populariteit handgebruik ontmoeting extravert blozen
1 11 5 6 5 7
2 11 2 6 2 3
3 15 6 6 6 5
4 9 6 4 4 5
5 11 6 2 6 3
6 17 5 7 3 4
7 16 5 6 5 4
8 9 6 5 3 5
9 14 6 6 5 5
10 12 5 7 4 5
11 6 5 7 1 6
12 4 5 4 6 5
13 13 6 1 6 2
14 12 5 6 6 5
15 10 5 4 4 4
16 14 6 5 6 6
17 12 6 5 5 5
18 9 4 6 3 6
19 16 5 4 5 5
20 13 5 6 4 2
21 12 5 3 5 3
22 11 6 3 6 5
23 12 5 5 3 6
24 12 7 5 4 5
25 11 6 5 5 4
26 16 6 5 4 5
27 9 6 5 5 5
28 8 6 2 6 5
29 11 4 6 7 5
30 9 5 7 2 6
31 16 6 2 4 6
32 14 4 3 6 6
33 10 5 6 5 6
34 14 5 5 5 4
35 13 5 7 5 4
36 12 7 5 6 3
37 16 7 6 6 5
38 16 6 5 1 6
39 15 7 3 4 4
40 5 6 7 2 6
41 12 5 5 3 3
42 11 6 5 4 2
43 15 4 6 5 5
44 15 6 2 4 5
45 10 5 3 3 6
46 12 6 6 4 4
47 5 6 7 6 3
48 16 5 5 4 3
49 16 6 4 5 4
50 12 5 6 4 5
51 6 5 7 5 4
52 7 5 2 6 3
53 14 6 2 6 4
54 8 6 2 4 4
55 12 5 5 4 4
56 10 7 2 6 3
57 11 6 5 4 6
58 17 5 6 2 5
59 13 5 2 6 5
60 15 6 4 5 6
61 10 5 6 6 6
62 9 5 4 6 4
63 16 6 3 5 5
64 11 6 3 5 4
65 8 3 3 5 6
66 14 5 6 5 5
67 11 5 6 3 5
68 12 6 5 4 5
69 14 5 3 1 5
70 15 5 3 5 2
71 14 4 2 2 5
72 11 5 3 6 5
73 11 5 3 5 5
74 15 2 5 2 2
75 7 6 3 6 6
76 12 6 5 5 4
77 10 6 2 6 4
78 13 6 5 3 6
79 15 5 6 4 6
80 13 5 6 4 4
81 15 6 5 4 2
82 8 5 2 4 4
83 14 5 6 5 5
84 11 6 7 2 7
85 12 3 5 3 7
86 16 6 5 5 5
87 8 3 2 6 5
88 12 5 5 5 5
89 16 5 6 6 4
90 11 6 5 3 6
91 13 5 5 4 5
92 6 6 4 4 4
93 4 6 5 3 6
94 11 6 4 4 4
95 7 5 3 4 4
96 12 3 5 2 5
97 12 4 2 6 2
98 16 7 2 3 5
99 15 6 4 5 5
100 13 6 3 5 5
101 12 5 5 5 6
102 9 4 5 5 5
103 16 6 2 4 4
104 11 6 5 2 5
105 14 6 2 5 5
106 10 5 6 3 5
107 10 6 2 6 5
108 11 6 1 6 4
109 16 2 6 1 1
110 8 6 2 7 5
111 16 5 3 5 3
112 12 5 5 6 5
113 11 3 4 6 5
114 16 4 4 6 6
115 9 6 6 3 5
116 13 5 2 6 4
117 14 6 7 7 6
118 10 4 2 6 2
119 12 6 5 5 2
120 11 4 3 5 4
121 10 3 3 5 6
122 12 6 5 5 5
123 13 5 5 4 4
124 14 7 4 4 5
125 12 6 3 6 5
126 14 6 2 6 5
127 13 5 6 4 4
128 8 5 2 7 2
129 13 2 6 3 6
130 10 5 6 4 5
131 9 3 2 2 4
132 8 6 5 4 5
133 15 5 6 4 5
134 15 5 5 3 5
135 12 5 3 2 5
136 8 2 7 5 6
137 15 5 5 5 2
138 9 5 4 4 4
139 14 6 5 6 7
140 16 6 3 5 3
141 14 5 2 1 2
142 14 5 5 5 5
143 14 5 5 5 3
144 14 6 2 5 5
145 14 6 3 5 6
146 13 6 2 5 3
147 12 6 6 4 5
148 13 6 6 7 5
149 19 7 2 5 3
150 8 6 3 6 3
151 10 6 4 3 5
152 7 6 6 5 6
153 12 7 2 6 5
154 16 1 7 1 6
155 15 6 2 6 3
156 9 5 2 4 5
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) handgebruik ontmoeting extravert blozen
12.93870 0.19552 0.04942 -0.16377 -0.31690
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.96619 -1.96362 0.04445 2.30607 6.36339
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.93870 1.75382 7.377 1e-11 ***
handgebruik 0.19552 0.22548 0.867 0.387
ontmoeting 0.04942 0.15623 0.316 0.752
extravert -0.16377 0.17918 -0.914 0.362
blozen -0.31690 0.20064 -1.579 0.116
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.969 on 151 degrees of freedom
Multiple R-squared: 0.02324, Adjusted R-squared: -0.002637
F-statistic: 0.8981 on 4 and 151 DF, p-value: 0.4668
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.09376212 0.18752423 0.90623788
[2,] 0.03977357 0.07954714 0.96022643
[3,] 0.07517033 0.15034066 0.92482967
[4,] 0.06340800 0.12681600 0.93659200
[5,] 0.43482201 0.86964402 0.56517799
[6,] 0.37831781 0.75663562 0.62168219
[7,] 0.30531519 0.61063038 0.69468481
[8,] 0.22642740 0.45285480 0.77357260
[9,] 0.30802008 0.61604015 0.69197992
[10,] 0.23121114 0.46242228 0.76878886
[11,] 0.20967687 0.41935374 0.79032313
[12,] 0.55551431 0.88897139 0.44448569
[13,] 0.56758117 0.86483765 0.43241883
[14,] 0.49327470 0.98654939 0.50672530
[15,] 0.41983570 0.83967141 0.58016430
[16,] 0.44455080 0.88910159 0.55544920
[17,] 0.37446076 0.74892153 0.62553924
[18,] 0.34355826 0.68711652 0.65644174
[19,] 0.43612112 0.87224224 0.56387888
[20,] 0.44888085 0.89776169 0.55111915
[21,] 0.41579203 0.83158405 0.58420797
[22,] 0.37790032 0.75580063 0.62209968
[23,] 0.34329214 0.68658428 0.65670786
[24,] 0.62359564 0.75280871 0.37640436
[25,] 0.65094257 0.69811485 0.34905743
[26,] 0.60918462 0.78163076 0.39081538
[27,] 0.57224207 0.85551586 0.42775793
[28,] 0.51512313 0.96975375 0.48487687
[29,] 0.47121325 0.94242650 0.52878675
[30,] 0.47969587 0.95939175 0.52030413
[31,] 0.57042781 0.85914439 0.42957219
[32,] 0.54666905 0.90666190 0.45333095
[33,] 0.75494645 0.49010710 0.24505355
[34,] 0.71157034 0.57685932 0.28842966
[35,] 0.68710435 0.62579130 0.31289565
[36,] 0.69947550 0.60104901 0.30052450
[37,] 0.70321576 0.59356849 0.29678424
[38,] 0.66593439 0.66813122 0.33406561
[39,] 0.61809069 0.76381863 0.38190931
[40,] 0.83527493 0.32945013 0.16472507
[41,] 0.84906291 0.30187419 0.15093709
[42,] 0.86047600 0.27904800 0.13952400
[43,] 0.83106951 0.33786098 0.16893049
[44,] 0.90603400 0.18793200 0.09396600
[45,] 0.94277410 0.11445179 0.05722590
[46,] 0.93302016 0.13395967 0.06697984
[47,] 0.94763023 0.10473953 0.05236977
[48,] 0.93370492 0.13259015 0.06629508
[49,] 0.92910162 0.14179677 0.07089838
[50,] 0.91271168 0.17457664 0.08728832
[51,] 0.93974828 0.12050344 0.06025172
[52,] 0.92811507 0.14376985 0.07188493
[53,] 0.93049778 0.13900444 0.06950222
[54,] 0.91765116 0.16469767 0.08234884
[55,] 0.91591838 0.16816324 0.08408162
[56,] 0.92967287 0.14065426 0.07032713
[57,] 0.91519690 0.16960620 0.08480310
[58,] 0.91297393 0.17405213 0.08702607
[59,] 0.90370997 0.19258006 0.09629003
[60,] 0.88554584 0.22890832 0.11445416
[61,] 0.86118954 0.27762091 0.13881046
[62,] 0.84420278 0.31159443 0.15579722
[63,] 0.83615372 0.32769257 0.16384628
[64,] 0.82372039 0.35255923 0.17627961
[65,] 0.79270781 0.41458438 0.20729219
[66,] 0.75936195 0.48127610 0.24063805
[67,] 0.74887595 0.50224810 0.25112405
[68,] 0.78858089 0.42283822 0.21141911
[69,] 0.75423915 0.49152169 0.24576085
[70,] 0.73265317 0.53469366 0.26734683
[71,] 0.69720292 0.60559415 0.30279708
[72,] 0.70519147 0.58961706 0.29480853
[73,] 0.66559745 0.66880509 0.33440255
[74,] 0.63756165 0.72487671 0.36243835
[75,] 0.67268160 0.65463680 0.32731840
[76,] 0.65074940 0.69850120 0.34925060
[77,] 0.61059445 0.77881111 0.38940555
[78,] 0.57009975 0.85980051 0.42990025
[79,] 0.60429514 0.79140973 0.39570486
[80,] 0.60481212 0.79037575 0.39518788
[81,] 0.55876795 0.88246410 0.44123205
[82,] 0.59333833 0.81332335 0.40666167
[83,] 0.55075385 0.89849231 0.44924615
[84,] 0.50956552 0.98086896 0.49043448
[85,] 0.66990701 0.66018599 0.33009299
[86,] 0.88657097 0.22685806 0.11342903
[87,] 0.86926430 0.26147139 0.13073570
[88,] 0.91916087 0.16167826 0.08083913
[89,] 0.89932408 0.20135184 0.10067592
[90,] 0.87589995 0.24820009 0.12410005
[91,] 0.88352327 0.23295345 0.11647673
[92,] 0.88329578 0.23340845 0.11670422
[93,] 0.86025857 0.27948287 0.13974143
[94,] 0.83115942 0.33768117 0.16884058
[95,] 0.82606032 0.34787936 0.17393968
[96,] 0.84194854 0.31610292 0.15805146
[97,] 0.82036863 0.35926274 0.17963137
[98,] 0.80436511 0.39126977 0.19563489
[99,] 0.79488916 0.41022169 0.20511084
[100,] 0.76824910 0.46350180 0.23175090
[101,] 0.73038448 0.53923103 0.26961552
[102,] 0.72244081 0.55511838 0.27755919
[103,] 0.75246892 0.49506216 0.24753108
[104,] 0.77543875 0.44912251 0.22456125
[105,] 0.73360068 0.53279864 0.26639932
[106,] 0.68822619 0.62354762 0.31177381
[107,] 0.75672840 0.48654320 0.24327160
[108,] 0.79188577 0.41622845 0.20811423
[109,] 0.76071447 0.47857106 0.23928553
[110,] 0.74143590 0.51712820 0.25856410
[111,] 0.70796935 0.58406130 0.29203065
[112,] 0.66584614 0.66830772 0.33415386
[113,] 0.61388297 0.77223407 0.38611703
[114,] 0.55968579 0.88062843 0.44031421
[115,] 0.50233743 0.99532514 0.49766257
[116,] 0.44443207 0.88886413 0.55556793
[117,] 0.39404390 0.78808780 0.60595610
[118,] 0.33710685 0.67421370 0.66289315
[119,] 0.31450684 0.62901369 0.68549316
[120,] 0.26208856 0.52417712 0.73791144
[121,] 0.32323166 0.64646332 0.67676834
[122,] 0.28578894 0.57157788 0.71421106
[123,] 0.25941055 0.51882110 0.74058945
[124,] 0.27807258 0.55614516 0.72192742
[125,] 0.34349160 0.68698320 0.65650840
[126,] 0.32315249 0.64630498 0.67684751
[127,] 0.30391506 0.60783011 0.69608494
[128,] 0.24572304 0.49144609 0.75427696
[129,] 0.29476727 0.58953453 0.70523273
[130,] 0.24042212 0.48084424 0.75957788
[131,] 0.28965394 0.57930787 0.71034606
[132,] 0.28140987 0.56281974 0.71859013
[133,] 0.25522155 0.51044309 0.74477845
[134,] 0.19491476 0.38982952 0.80508524
[135,] 0.15238053 0.30476106 0.84761947
[136,] 0.10381346 0.20762691 0.89618654
[137,] 0.07462245 0.14924490 0.92537755
[138,] 0.08350102 0.16700204 0.91649898
[139,] 0.05085437 0.10170874 0.94914563
[140,] 0.02585647 0.05171295 0.97414353
[141,] 0.02570907 0.05141815 0.97429093
> postscript(file="/var/www/html/rcomp/tmp/1zzhy1291386862.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2zzhy1291386862.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3zzhy1291386862.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4a8yj1291386862.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5a8yj1291386862.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 156
Frequency = 1
1 2 3 4 5 6
-0.17564507 -1.34800973 3.15880591 -3.06989747 -1.27731833 4.49668946
7 8 9 10 11 12
3.87365141 -3.28308821 1.99503469 -0.02263815 -6.19705063 -7.54683715
13 14 15 16 17 18
0.45520002 0.35432380 -2.19128075 2.52512660 0.04445422 -2.62457078
19 20 21 22 23 24
4.28939164 0.07607786 -0.29499118 -0.69293552 0.22933085 -0.31483489
25 26 27 28 29 30
-1.27244695 3.88068300 -2.95554578 -3.64351599 -0.28638709 -3.03327941
31 32 33 34 35 36
4.34584275 3.01500144 -1.49254624 1.92307094 0.82423189 -0.62109480
37 38 39 40 41 42
3.96328802 3.70627053 2.46710299 -7.22879730 -0.72137266 -2.07002051
43 44 45 46 47 48
3.38607048 3.02894158 -1.67183010 -0.48563769 -7.52441596 3.44239855
49 50 51 52 53 54
3.77697257 0.02678137 -6.17576811 -5.08180044 2.03958284 -4.28795959
55 56 57 58 59 60
-0.24070028 -2.47283623 -0.80241582 4.69923894 1.55200190 3.41077492
61 62 63 64 65 66
-1.32877503 -2.86373832 4.14329327 -1.17360790 -2.95325189 2.19055259
67 68 69 70 71 72
-1.13698984 -0.11931700 1.68372630 2.38810765 2.09243493 -0.49741762
73 74 75 76 77 78
-0.66118884 2.38450863 -4.37603435 -0.27244695 -1.96041716 1.03381296
79 80 81 82 83 84
3.34368254 0.70988020 1.92997949 -4.09244170 2.19055259 -0.91189613
85 86 87 88 89 90
0.93726781 4.04445422 -3.05696232 0.23997211 4.03742263 -0.96618704
91 92 93 94 95 96
1.07620090 -6.38679864 -7.96618704 -1.38679864 -5.14186123 0.13969425
97 98 99 100 101 102
-0.20318372 3.66965247 3.09387374 1.14329327 0.55687328 -2.56451000
103 104 105 106 107 108
3.71204041 -1.44685942 2.19271279 -2.13698984 -1.64351599 -0.91099764
109 110 111 112 113 114
2.85441672 -3.47974478 3.70500882 0.40374333 -0.15580137 4.96558191
115 116 117 118 119 120
-3.33250774 1.23510073 2.59005877 -2.20318372 -0.90624929 -0.78257212
121 122 123 124 125 126
-0.95325189 0.04445422 0.75929972 1.73458464 0.30706448 2.35648401
127 128 129 130 131 132
0.70988020 -4.23493040 1.76646500 -1.97321863 -3.02894835 -4.11931700
133 134 135 136 137 138
3.02678137 2.91242968 -0.15250248 -2.95541209 2.28926860 -3.19128075
139 140 141 142 143 144
2.84202778 3.50949093 0.78244231 2.23997211 1.60616977 2.19271279
145 146 147 148 149 150
2.46019444 0.55891045 -0.16873652 1.32257712 6.36339256 -4.32673786
151 152 153 154 155 156
-2.23366869 -4.68806413 0.16096612 4.58502094 2.72268167 -2.77554053
> postscript(file="/var/www/html/rcomp/tmp/6a8yj1291386862.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.17564507 NA
1 -1.34800973 -0.17564507
2 3.15880591 -1.34800973
3 -3.06989747 3.15880591
4 -1.27731833 -3.06989747
5 4.49668946 -1.27731833
6 3.87365141 4.49668946
7 -3.28308821 3.87365141
8 1.99503469 -3.28308821
9 -0.02263815 1.99503469
10 -6.19705063 -0.02263815
11 -7.54683715 -6.19705063
12 0.45520002 -7.54683715
13 0.35432380 0.45520002
14 -2.19128075 0.35432380
15 2.52512660 -2.19128075
16 0.04445422 2.52512660
17 -2.62457078 0.04445422
18 4.28939164 -2.62457078
19 0.07607786 4.28939164
20 -0.29499118 0.07607786
21 -0.69293552 -0.29499118
22 0.22933085 -0.69293552
23 -0.31483489 0.22933085
24 -1.27244695 -0.31483489
25 3.88068300 -1.27244695
26 -2.95554578 3.88068300
27 -3.64351599 -2.95554578
28 -0.28638709 -3.64351599
29 -3.03327941 -0.28638709
30 4.34584275 -3.03327941
31 3.01500144 4.34584275
32 -1.49254624 3.01500144
33 1.92307094 -1.49254624
34 0.82423189 1.92307094
35 -0.62109480 0.82423189
36 3.96328802 -0.62109480
37 3.70627053 3.96328802
38 2.46710299 3.70627053
39 -7.22879730 2.46710299
40 -0.72137266 -7.22879730
41 -2.07002051 -0.72137266
42 3.38607048 -2.07002051
43 3.02894158 3.38607048
44 -1.67183010 3.02894158
45 -0.48563769 -1.67183010
46 -7.52441596 -0.48563769
47 3.44239855 -7.52441596
48 3.77697257 3.44239855
49 0.02678137 3.77697257
50 -6.17576811 0.02678137
51 -5.08180044 -6.17576811
52 2.03958284 -5.08180044
53 -4.28795959 2.03958284
54 -0.24070028 -4.28795959
55 -2.47283623 -0.24070028
56 -0.80241582 -2.47283623
57 4.69923894 -0.80241582
58 1.55200190 4.69923894
59 3.41077492 1.55200190
60 -1.32877503 3.41077492
61 -2.86373832 -1.32877503
62 4.14329327 -2.86373832
63 -1.17360790 4.14329327
64 -2.95325189 -1.17360790
65 2.19055259 -2.95325189
66 -1.13698984 2.19055259
67 -0.11931700 -1.13698984
68 1.68372630 -0.11931700
69 2.38810765 1.68372630
70 2.09243493 2.38810765
71 -0.49741762 2.09243493
72 -0.66118884 -0.49741762
73 2.38450863 -0.66118884
74 -4.37603435 2.38450863
75 -0.27244695 -4.37603435
76 -1.96041716 -0.27244695
77 1.03381296 -1.96041716
78 3.34368254 1.03381296
79 0.70988020 3.34368254
80 1.92997949 0.70988020
81 -4.09244170 1.92997949
82 2.19055259 -4.09244170
83 -0.91189613 2.19055259
84 0.93726781 -0.91189613
85 4.04445422 0.93726781
86 -3.05696232 4.04445422
87 0.23997211 -3.05696232
88 4.03742263 0.23997211
89 -0.96618704 4.03742263
90 1.07620090 -0.96618704
91 -6.38679864 1.07620090
92 -7.96618704 -6.38679864
93 -1.38679864 -7.96618704
94 -5.14186123 -1.38679864
95 0.13969425 -5.14186123
96 -0.20318372 0.13969425
97 3.66965247 -0.20318372
98 3.09387374 3.66965247
99 1.14329327 3.09387374
100 0.55687328 1.14329327
101 -2.56451000 0.55687328
102 3.71204041 -2.56451000
103 -1.44685942 3.71204041
104 2.19271279 -1.44685942
105 -2.13698984 2.19271279
106 -1.64351599 -2.13698984
107 -0.91099764 -1.64351599
108 2.85441672 -0.91099764
109 -3.47974478 2.85441672
110 3.70500882 -3.47974478
111 0.40374333 3.70500882
112 -0.15580137 0.40374333
113 4.96558191 -0.15580137
114 -3.33250774 4.96558191
115 1.23510073 -3.33250774
116 2.59005877 1.23510073
117 -2.20318372 2.59005877
118 -0.90624929 -2.20318372
119 -0.78257212 -0.90624929
120 -0.95325189 -0.78257212
121 0.04445422 -0.95325189
122 0.75929972 0.04445422
123 1.73458464 0.75929972
124 0.30706448 1.73458464
125 2.35648401 0.30706448
126 0.70988020 2.35648401
127 -4.23493040 0.70988020
128 1.76646500 -4.23493040
129 -1.97321863 1.76646500
130 -3.02894835 -1.97321863
131 -4.11931700 -3.02894835
132 3.02678137 -4.11931700
133 2.91242968 3.02678137
134 -0.15250248 2.91242968
135 -2.95541209 -0.15250248
136 2.28926860 -2.95541209
137 -3.19128075 2.28926860
138 2.84202778 -3.19128075
139 3.50949093 2.84202778
140 0.78244231 3.50949093
141 2.23997211 0.78244231
142 1.60616977 2.23997211
143 2.19271279 1.60616977
144 2.46019444 2.19271279
145 0.55891045 2.46019444
146 -0.16873652 0.55891045
147 1.32257712 -0.16873652
148 6.36339256 1.32257712
149 -4.32673786 6.36339256
150 -2.23366869 -4.32673786
151 -4.68806413 -2.23366869
152 0.16096612 -4.68806413
153 4.58502094 0.16096612
154 2.72268167 4.58502094
155 -2.77554053 2.72268167
156 NA -2.77554053
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.34800973 -0.17564507
[2,] 3.15880591 -1.34800973
[3,] -3.06989747 3.15880591
[4,] -1.27731833 -3.06989747
[5,] 4.49668946 -1.27731833
[6,] 3.87365141 4.49668946
[7,] -3.28308821 3.87365141
[8,] 1.99503469 -3.28308821
[9,] -0.02263815 1.99503469
[10,] -6.19705063 -0.02263815
[11,] -7.54683715 -6.19705063
[12,] 0.45520002 -7.54683715
[13,] 0.35432380 0.45520002
[14,] -2.19128075 0.35432380
[15,] 2.52512660 -2.19128075
[16,] 0.04445422 2.52512660
[17,] -2.62457078 0.04445422
[18,] 4.28939164 -2.62457078
[19,] 0.07607786 4.28939164
[20,] -0.29499118 0.07607786
[21,] -0.69293552 -0.29499118
[22,] 0.22933085 -0.69293552
[23,] -0.31483489 0.22933085
[24,] -1.27244695 -0.31483489
[25,] 3.88068300 -1.27244695
[26,] -2.95554578 3.88068300
[27,] -3.64351599 -2.95554578
[28,] -0.28638709 -3.64351599
[29,] -3.03327941 -0.28638709
[30,] 4.34584275 -3.03327941
[31,] 3.01500144 4.34584275
[32,] -1.49254624 3.01500144
[33,] 1.92307094 -1.49254624
[34,] 0.82423189 1.92307094
[35,] -0.62109480 0.82423189
[36,] 3.96328802 -0.62109480
[37,] 3.70627053 3.96328802
[38,] 2.46710299 3.70627053
[39,] -7.22879730 2.46710299
[40,] -0.72137266 -7.22879730
[41,] -2.07002051 -0.72137266
[42,] 3.38607048 -2.07002051
[43,] 3.02894158 3.38607048
[44,] -1.67183010 3.02894158
[45,] -0.48563769 -1.67183010
[46,] -7.52441596 -0.48563769
[47,] 3.44239855 -7.52441596
[48,] 3.77697257 3.44239855
[49,] 0.02678137 3.77697257
[50,] -6.17576811 0.02678137
[51,] -5.08180044 -6.17576811
[52,] 2.03958284 -5.08180044
[53,] -4.28795959 2.03958284
[54,] -0.24070028 -4.28795959
[55,] -2.47283623 -0.24070028
[56,] -0.80241582 -2.47283623
[57,] 4.69923894 -0.80241582
[58,] 1.55200190 4.69923894
[59,] 3.41077492 1.55200190
[60,] -1.32877503 3.41077492
[61,] -2.86373832 -1.32877503
[62,] 4.14329327 -2.86373832
[63,] -1.17360790 4.14329327
[64,] -2.95325189 -1.17360790
[65,] 2.19055259 -2.95325189
[66,] -1.13698984 2.19055259
[67,] -0.11931700 -1.13698984
[68,] 1.68372630 -0.11931700
[69,] 2.38810765 1.68372630
[70,] 2.09243493 2.38810765
[71,] -0.49741762 2.09243493
[72,] -0.66118884 -0.49741762
[73,] 2.38450863 -0.66118884
[74,] -4.37603435 2.38450863
[75,] -0.27244695 -4.37603435
[76,] -1.96041716 -0.27244695
[77,] 1.03381296 -1.96041716
[78,] 3.34368254 1.03381296
[79,] 0.70988020 3.34368254
[80,] 1.92997949 0.70988020
[81,] -4.09244170 1.92997949
[82,] 2.19055259 -4.09244170
[83,] -0.91189613 2.19055259
[84,] 0.93726781 -0.91189613
[85,] 4.04445422 0.93726781
[86,] -3.05696232 4.04445422
[87,] 0.23997211 -3.05696232
[88,] 4.03742263 0.23997211
[89,] -0.96618704 4.03742263
[90,] 1.07620090 -0.96618704
[91,] -6.38679864 1.07620090
[92,] -7.96618704 -6.38679864
[93,] -1.38679864 -7.96618704
[94,] -5.14186123 -1.38679864
[95,] 0.13969425 -5.14186123
[96,] -0.20318372 0.13969425
[97,] 3.66965247 -0.20318372
[98,] 3.09387374 3.66965247
[99,] 1.14329327 3.09387374
[100,] 0.55687328 1.14329327
[101,] -2.56451000 0.55687328
[102,] 3.71204041 -2.56451000
[103,] -1.44685942 3.71204041
[104,] 2.19271279 -1.44685942
[105,] -2.13698984 2.19271279
[106,] -1.64351599 -2.13698984
[107,] -0.91099764 -1.64351599
[108,] 2.85441672 -0.91099764
[109,] -3.47974478 2.85441672
[110,] 3.70500882 -3.47974478
[111,] 0.40374333 3.70500882
[112,] -0.15580137 0.40374333
[113,] 4.96558191 -0.15580137
[114,] -3.33250774 4.96558191
[115,] 1.23510073 -3.33250774
[116,] 2.59005877 1.23510073
[117,] -2.20318372 2.59005877
[118,] -0.90624929 -2.20318372
[119,] -0.78257212 -0.90624929
[120,] -0.95325189 -0.78257212
[121,] 0.04445422 -0.95325189
[122,] 0.75929972 0.04445422
[123,] 1.73458464 0.75929972
[124,] 0.30706448 1.73458464
[125,] 2.35648401 0.30706448
[126,] 0.70988020 2.35648401
[127,] -4.23493040 0.70988020
[128,] 1.76646500 -4.23493040
[129,] -1.97321863 1.76646500
[130,] -3.02894835 -1.97321863
[131,] -4.11931700 -3.02894835
[132,] 3.02678137 -4.11931700
[133,] 2.91242968 3.02678137
[134,] -0.15250248 2.91242968
[135,] -2.95541209 -0.15250248
[136,] 2.28926860 -2.95541209
[137,] -3.19128075 2.28926860
[138,] 2.84202778 -3.19128075
[139,] 3.50949093 2.84202778
[140,] 0.78244231 3.50949093
[141,] 2.23997211 0.78244231
[142,] 1.60616977 2.23997211
[143,] 2.19271279 1.60616977
[144,] 2.46019444 2.19271279
[145,] 0.55891045 2.46019444
[146,] -0.16873652 0.55891045
[147,] 1.32257712 -0.16873652
[148,] 6.36339256 1.32257712
[149,] -4.32673786 6.36339256
[150,] -2.23366869 -4.32673786
[151,] -4.68806413 -2.23366869
[152,] 0.16096612 -4.68806413
[153,] 4.58502094 0.16096612
[154,] 2.72268167 4.58502094
[155,] -2.77554053 2.72268167
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.34800973 -0.17564507
2 3.15880591 -1.34800973
3 -3.06989747 3.15880591
4 -1.27731833 -3.06989747
5 4.49668946 -1.27731833
6 3.87365141 4.49668946
7 -3.28308821 3.87365141
8 1.99503469 -3.28308821
9 -0.02263815 1.99503469
10 -6.19705063 -0.02263815
11 -7.54683715 -6.19705063
12 0.45520002 -7.54683715
13 0.35432380 0.45520002
14 -2.19128075 0.35432380
15 2.52512660 -2.19128075
16 0.04445422 2.52512660
17 -2.62457078 0.04445422
18 4.28939164 -2.62457078
19 0.07607786 4.28939164
20 -0.29499118 0.07607786
21 -0.69293552 -0.29499118
22 0.22933085 -0.69293552
23 -0.31483489 0.22933085
24 -1.27244695 -0.31483489
25 3.88068300 -1.27244695
26 -2.95554578 3.88068300
27 -3.64351599 -2.95554578
28 -0.28638709 -3.64351599
29 -3.03327941 -0.28638709
30 4.34584275 -3.03327941
31 3.01500144 4.34584275
32 -1.49254624 3.01500144
33 1.92307094 -1.49254624
34 0.82423189 1.92307094
35 -0.62109480 0.82423189
36 3.96328802 -0.62109480
37 3.70627053 3.96328802
38 2.46710299 3.70627053
39 -7.22879730 2.46710299
40 -0.72137266 -7.22879730
41 -2.07002051 -0.72137266
42 3.38607048 -2.07002051
43 3.02894158 3.38607048
44 -1.67183010 3.02894158
45 -0.48563769 -1.67183010
46 -7.52441596 -0.48563769
47 3.44239855 -7.52441596
48 3.77697257 3.44239855
49 0.02678137 3.77697257
50 -6.17576811 0.02678137
51 -5.08180044 -6.17576811
52 2.03958284 -5.08180044
53 -4.28795959 2.03958284
54 -0.24070028 -4.28795959
55 -2.47283623 -0.24070028
56 -0.80241582 -2.47283623
57 4.69923894 -0.80241582
58 1.55200190 4.69923894
59 3.41077492 1.55200190
60 -1.32877503 3.41077492
61 -2.86373832 -1.32877503
62 4.14329327 -2.86373832
63 -1.17360790 4.14329327
64 -2.95325189 -1.17360790
65 2.19055259 -2.95325189
66 -1.13698984 2.19055259
67 -0.11931700 -1.13698984
68 1.68372630 -0.11931700
69 2.38810765 1.68372630
70 2.09243493 2.38810765
71 -0.49741762 2.09243493
72 -0.66118884 -0.49741762
73 2.38450863 -0.66118884
74 -4.37603435 2.38450863
75 -0.27244695 -4.37603435
76 -1.96041716 -0.27244695
77 1.03381296 -1.96041716
78 3.34368254 1.03381296
79 0.70988020 3.34368254
80 1.92997949 0.70988020
81 -4.09244170 1.92997949
82 2.19055259 -4.09244170
83 -0.91189613 2.19055259
84 0.93726781 -0.91189613
85 4.04445422 0.93726781
86 -3.05696232 4.04445422
87 0.23997211 -3.05696232
88 4.03742263 0.23997211
89 -0.96618704 4.03742263
90 1.07620090 -0.96618704
91 -6.38679864 1.07620090
92 -7.96618704 -6.38679864
93 -1.38679864 -7.96618704
94 -5.14186123 -1.38679864
95 0.13969425 -5.14186123
96 -0.20318372 0.13969425
97 3.66965247 -0.20318372
98 3.09387374 3.66965247
99 1.14329327 3.09387374
100 0.55687328 1.14329327
101 -2.56451000 0.55687328
102 3.71204041 -2.56451000
103 -1.44685942 3.71204041
104 2.19271279 -1.44685942
105 -2.13698984 2.19271279
106 -1.64351599 -2.13698984
107 -0.91099764 -1.64351599
108 2.85441672 -0.91099764
109 -3.47974478 2.85441672
110 3.70500882 -3.47974478
111 0.40374333 3.70500882
112 -0.15580137 0.40374333
113 4.96558191 -0.15580137
114 -3.33250774 4.96558191
115 1.23510073 -3.33250774
116 2.59005877 1.23510073
117 -2.20318372 2.59005877
118 -0.90624929 -2.20318372
119 -0.78257212 -0.90624929
120 -0.95325189 -0.78257212
121 0.04445422 -0.95325189
122 0.75929972 0.04445422
123 1.73458464 0.75929972
124 0.30706448 1.73458464
125 2.35648401 0.30706448
126 0.70988020 2.35648401
127 -4.23493040 0.70988020
128 1.76646500 -4.23493040
129 -1.97321863 1.76646500
130 -3.02894835 -1.97321863
131 -4.11931700 -3.02894835
132 3.02678137 -4.11931700
133 2.91242968 3.02678137
134 -0.15250248 2.91242968
135 -2.95541209 -0.15250248
136 2.28926860 -2.95541209
137 -3.19128075 2.28926860
138 2.84202778 -3.19128075
139 3.50949093 2.84202778
140 0.78244231 3.50949093
141 2.23997211 0.78244231
142 1.60616977 2.23997211
143 2.19271279 1.60616977
144 2.46019444 2.19271279
145 0.55891045 2.46019444
146 -0.16873652 0.55891045
147 1.32257712 -0.16873652
148 6.36339256 1.32257712
149 -4.32673786 6.36339256
150 -2.23366869 -4.32673786
151 -4.68806413 -2.23366869
152 0.16096612 -4.68806413
153 4.58502094 0.16096612
154 2.72268167 4.58502094
155 -2.77554053 2.72268167
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/73hg41291386862.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/83hg41291386862.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/96jzk1291386863.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/106jzk1291386863.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11r1yq1291386863.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12v2fv1291386863.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13jlu71291386863.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14cubs1291386863.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15yurg1291386863.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16umpp1291386863.tab")
+ }
>
> try(system("convert tmp/1zzhy1291386862.ps tmp/1zzhy1291386862.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zzhy1291386862.ps tmp/2zzhy1291386862.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zzhy1291386862.ps tmp/3zzhy1291386862.png",intern=TRUE))
character(0)
> try(system("convert tmp/4a8yj1291386862.ps tmp/4a8yj1291386862.png",intern=TRUE))
character(0)
> try(system("convert tmp/5a8yj1291386862.ps tmp/5a8yj1291386862.png",intern=TRUE))
character(0)
> try(system("convert tmp/6a8yj1291386862.ps tmp/6a8yj1291386862.png",intern=TRUE))
character(0)
> try(system("convert tmp/73hg41291386862.ps tmp/73hg41291386862.png",intern=TRUE))
character(0)
> try(system("convert tmp/83hg41291386862.ps tmp/83hg41291386862.png",intern=TRUE))
character(0)
> try(system("convert tmp/96jzk1291386863.ps tmp/96jzk1291386863.png",intern=TRUE))
character(0)
> try(system("convert tmp/106jzk1291386863.ps tmp/106jzk1291386863.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.946 1.784 9.328